Transcript Intro-1-fall08

Welcome to CompSci 171 spring 2007
Introduction to AI.
http://www.ics.uci.edu/~welling/teaching/ICS171spring07/ICS171spring07.html
Instructor:
Max Welling,
Office hours:
Teaching Assistant:
Tuan Nguyen
Reader:
Natalia Flerova
Book:
[email protected]
Fr. 12-1pm in BH 4028
Artificial Intelligence, A Modern Approach
Russell & Norvig
Prentice Hall
ICS-171:Notes 1: 1
• Grading:
-Homework (0%, required)
-Quizzes (each other week) (20%)
-One project (20%)
-A midterm (20%)
-A Final Exam (40%)
Graded Quizzes/Exams
-Will be distributed and discussed in class
Grading Disputes:
Turn in your work for regrading at the discussion section to the TA within 1 week.
Note: we will regrade the entire paper: so your new grade could be higher or lower.
Course related issues can be addressed in the first 10 minutes of every class.
ICS-171:Notes 1: 2
Academic (Dis)Honesty
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It is each student’s responsibility to be familiar with UCI’s current
policies on academic honesty
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Violations can result in getting an F in the class (or worse)
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Please take the time to read the UCI academic honesty policy
– in the Fall Quarter schedule of classes
– or at: http://www.reg.uci.edu/REGISTRAR/SOC/adh.html
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Academic dishonesty is defined as:
– Cheating
– Dishonest conduct
– Plagiarism
– Collusion
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Syllabus:
Lecture 1. Introduction: Goals, history (Ch.1)
Lecture 2. Agents (Ch.2)
Lecture 3-4. Uninformed Search (Ch.3)
Lecture 5-6 Informed Search (Ch.4)
Lecture 7-8. Constraint satisfaction (Ch.5).  Project
Lecture 9-10 Games (Ch.6)
Lecture 11. Midterm
Lecture 12-13. Propositional Logic (Ch.7)
Lecture 14-15. First Order Logic (Ch.8)
Lecture 16-17. Inference in logic (Ch.9)
Lecture 18 Uncertainty (Ch.13)
Lecture 19. Philosophical Foundations (Ch.26).
Lecture 20. AI Present and Future (Ch.27).
Final
This is a very rough syllabus. It is almost certainly the case that
we will deviate from this. Some chapters will be treated only partially.
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Meet HAL
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2001: A Space Odyssey
– classic science fiction movie from 1969
http://www.youtube.com/watch?v=LE1F7d6f1Qk
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HAL
– part of the story centers around an intelligent computer called HAL
– HAL is the “brains” of an intelligent spaceship
– in the movie, HAL can
• speak easily with the crew
• see and understand the emotions of the crew
• navigate the ship automatically
• diagnose on-board problems
• make life-and-death decisions
• display emotions
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In 1969 this was science fiction: is it still science fiction?
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Different Types of Artificial Intelligence
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Modeling exactly how humans actually think
– cognitive models of human reasoning
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Modeling exactly how humans actually act
– models of human behavior (what they do, not how they think)
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Modeling how ideal agents “should think”
– models of “rational” thought (formal logic)
– note: humans are often not rational!
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Modeling how ideal agents “should act”
– rational actions but not necessarily formal rational reasoning
– i.e., more of a black-box/engineering approach
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Modern AI focuses on the last definition
– we will also focus on this “engineering” approach
– success is judged by how well the agent performs
-- modern methods are also inspired by cognitive & neuroscience
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(how people think).
Acting humanly: Turing Test
• Turing (1950) "Computing machinery and intelligence":
• "Can machines think?"  "Can machines behave intelligently?"
• Operational test for intelligent behavior: the Imitation Game
• Suggested major components of AI:
- knowledge representation
- reasoning,
- language/image understanding,
- learning
Can you think of a theoretical system that could beat the Turing test
yet you wouldn’t find it very intelligent?
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Acting rationally: rational agent
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Rational behavior: Doing that was is expected to maximize
one’s “utility function” in this world.
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An agent is an entity that perceives and acts.
A rational agent acts rationally.
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This course is about designing rational agents
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Abstractly, an agent is a function from percept histories to actions:
[f: P*  A]
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For any given class of environments and tasks, we seek the agent
(or class of agents) with the best performance
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Caveat: computational limitations make perfect rationality
unachievable
 design best program for given machine resources
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Academic Disciplines important to AI.
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Philosophy
Logic, methods of reasoning, mind as physical
system, foundations of learning, language,
rationality.
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Mathematics
Formal representation and proof, algorithms,
computation, (un)decidability, (in)tractability,
probability.
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Economics
utility, decision theory, rational economic agents
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Neuroscience
neurons as information processing units.
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Psychology/
Cognitive Science
how do people behave, perceive, process
information, represent knowledge.
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Computer
engineering
building fast computers
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Control theory
design systems that maximize an objective
function over time
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Linguistics
knowledge representation, grammar
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History of AI
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1943
1950
1956
1950s
• 1965
• 1966—73
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1969—79
1980-1986-1987-1995--
McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
Dartmouth meeting: "Artificial Intelligence" adopted
Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
Robinson's complete algorithm for logical reasoning
AI discovers computational complexity
Neural network research almost disappears
Early development of knowledge-based systems
AI becomes an industry
Neural networks return to popularity
AI becomes a science
The emergence of intelligent agents
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State of the art
• Deep Blue defeated the reigning world chess champion Garry
Kasparov in 1997
• Proved a mathematical conjecture (Robbins conjecture)
unsolved for decades
• No hands across America (driving autonomously 98% of the time
from Pittsburgh to San Diego)
• During the 1991 Gulf War, US forces deployed an AI logistics
planning and scheduling program that involved up to 50,000
vehicles, cargo, and people
• NASA's on-board autonomous planning program controlled the
scheduling of operations for a spacecraft
• Proverb solves crossword puzzles better than most humans
• Stanford vehicle in Darpa challenge completed autonomously a
132 mile desert track in 6 hours 32 minutes.
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Consider what might be involved in building a
“intelligent” computer….
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What are the “components” that might be useful?
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Fast hardware?
Foolproof software?
Chess-playing at grandmaster level?
Speech interaction?
• speech synthesis
• speech recognition
• speech understanding
– Image recognition and understanding ?
– Learning?
– Planning and decision-making?
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Can we build hardware as complex as the brain?
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How complicated is our brain?
– a neuron, or nerve cell, is the basic information processing unit
– estimated to be on the order of 10 11 neurons in a human brain
– many more synapses (10 14) connecting these neurons
– cycle time: 10 -3 seconds (1 millisecond)
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How complex can we make computers?
– 106 or more transistors per CPU
– supercomputer: hundreds of CPUs, 10 9 bits of RAM
– cycle times: order of 10 - 8 seconds
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Conclusion
– YES: in the near future we can have computers with as many basic
processing elements as our brain, but with
• far fewer interconnections (wires or synapses) than the brain
• much faster updates than the brain
– but building hardware is very different from making a computer
behave like a brain!
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Must an Intelligent System be Foolproof?
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A “foolproof” system is one that never makes an error:
– Types of possible computer errors
• hardware errors, e.g., memory errors
• software errors, e.g., coding bugs
• “human-like” errors
– Clearly, hardware and software errors are possible in practice
– what about “human-like” errors?
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An intelligent system can make errors and still be intelligent
– humans are not right all of the time
– we learn and adapt from making mistakes
• e.g., consider learning to surf or ski
– we improve by taking risks and falling
– an intelligent system can learn in the same way
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Conclusion:
– NO: intelligent systems will not (and need not) be foolproof
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Can Computers play Humans at Chess?
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Chess Playing is a classic AI problem
– well-defined problem
– very complex: difficult for humans to play well
3000
2800
Garry Kasparov (current World Champion)
Points Ratings
2600
Deep Blue
Deep Thought
2400
2200
Ratings
2000
1800
1600
1400
1200
1966
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1971
1976
1981
1986
1991
1997
Conclusion: YES: today’s computers can beat even the best human
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Can Computers Talk?
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This is known as “speech synthesis”
– translate text to phonetic form
• e.g., “fictitious” -> fik-tish-es
– use pronunciation rules to map phonemes to actual sound
• e.g., “tish” -> sequence of basic audio sounds
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Difficulties
– sounds made by this “lookup” approach sound unnatural
– sounds are not independent
• e.g., “act” and “action”
• modern systems (e.g., at AT&T) can handle this pretty well
– a harder problem is emphasis, emotion, etc
• humans understand what they are saying
• machines don’t: so they sound unnatural
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Conclusion: NO, for complete sentences, but YES for individual words
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Can Computers Recognize Speech?
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Speech Recognition:
– mapping sounds from a microphone into a list of words.
– Hard problem: noise, more than one person talking,
occlusion, speech variability,..
– Even if we recognize each word, we may not understand its meaning.
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Recognizing single words from a small vocabulary
• systems can do this with high accuracy (order of 99%)
• e.g., directory inquiries
– limited vocabulary (area codes, city names)
– computer tries to recognize you first, if unsuccessful hands you
over to a human operator
– saves millions of dollars a year for the phone companies
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Recognizing human speech (ctd.)
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Recognizing normal speech is much more difficult
– speech is continuous: where are the boundaries between words?
• e.g., “John’s car has a flat tire”
– large vocabularies
• can be many tens of thousands of possible words
• we can use context to help figure out what someone said
– try telling a waiter in a restaurant:
“I would like some dream and sugar in my coffee”
– background noise, other speakers, accents, colds, etc
– on normal speech, modern systems are only about 60% accurate
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Conclusion: NO, normal speech is too complex to accurately
recognize, but YES for restricted problems
– (e.g., recent software for PC use by IBM, Dragon systems, etc)
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Can Computers Understand speech?
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Understanding is different to recognition:
– “Time flies like an arrow”
• assume the computer can recognize all the words
• but how could it understand it?
– 1. time passes quickly like an arrow?
– 2. command: time the flies the way an arrow times the flies
– 3. command: only time those flies which are like an arrow
– 4. “time-flies” are fond of arrows
• only 1. makes any sense, but how could a computer figure this
out?
– clearly humans use a lot of implicit commonsense
knowledge in communication
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Conclusion: NO, much of what we say is beyond the capabilities of a
computer to understand at present
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Can Computers Learn and Adapt ?
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Learning and Adaptation
– consider a computer learning to drive on the freeway
– we could code lots of rules about what to do
– and/or we could have it learn from experience
Darpa’s Grand Challenge. Stanford’s “Stanley” drove
150 without supervision in the Majove dessert
– machine learning allows computers to learn to do things without
explicit programming
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Conclusion: YES, computers can learn and adapt, when presented
with information in the appropriate way
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Can Computers “see”?
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Recognition v. Understanding (like Speech)
– Recognition and Understanding of Objects in a scene
• look around this room
• you can effortlessly recognize objects
• human brain can map 2d visual image to 3d “map”
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Why is visual recognition a hard problem?
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Conclusion: mostly NO: computers can only “see” certain types of
objects under limited circumstances: but YES for certain constrained
problems (e.g., face recognition)
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In the computer vision community
research compete to improve recognition
performance on standard datasets
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Can Computers plan and make decisions?
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Intelligence
– involves solving problems and making decisions and plans
– e.g., you want to visit your cousin in Boston
• you need to decide on dates, flights
• you need to get to the airport, etc
• involves a sequence of decisions, plans, and actions
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What makes planning hard?
– the world is not predictable:
• your flight is canceled or there’s a backup on the 405
– there is a potentially huge number of details
• do you consider all flights? all dates?
– no: commonsense constrains your solutions
– AI systems are only successful in constrained planning problems
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Conclusion: NO, real-world planning and decision-making is still
beyond the capabilities of modern computers
– exception: very well-defined, constrained problems: mission
planning for satelites.
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Intelligent Systems in Your Everyday Life
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Post Office
– automatic address recognition and sorting of mail
Banks
– automatic check readers, signature verification systems
– automated loan application classification
Telephone Companies
– automatic voice recognition for directory inquiries
Credit Card Companies
– automated fraud detection
Computer Companies
– automated diagnosis for help-desk applications
Netflix:
– movie recommendation
Google:
– Search Technology
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AI Applications: Consumer Marketing
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Have you ever used any kind of credit/ATM/store card while shopping?
– if so, you have very likely been “input” to an AI algorithm
All of this information is recorded digitally
Companies like Nielsen gather this information weekly and search for
patterns
– general changes in consumer behavior
– tracking responses to new products
– identifying customer segments: targeted marketing, e.g., they find
out that consumers with sports cars who buy textbooks respond well
to offers of new credit cards.
– Currently a very hot area in marketing
How do they do this?
– Algorithms (“data mining”) search data for patterns
– based on mathematical theories of learning
– completely impractical to do manually
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AI Applications: Identification Technologies
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ID cards
– e.g., ATM cards
– can be a nuisance and security risk:
• cards can be lost, stolen, passwords forgotten, etc
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Biometric Identification
– walk up to a locked door
• camera
• fingerprint device
• microphone
• iris scan
– computer uses your biometric signature for identification
• face, eyes, fingerprints, voice pattern, iris pattern
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AI Applications: Predicting the Stock Market
Value of
the Stock
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?
time in days
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The Prediction Problem
– given the past, predict the future
– very difficult problem!
– we can use learning algorithms to learn a predictive model from historical
data
• prob(increase at day t+1 | values at day t, t-1,t-2....,t-k)
– such models are routinely used by banks and financial traders to
manage portfolios worth millions of dollars
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AI-Applications: Machine Translation
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Language problems in international business
– e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish
investors, no common language
– or: you are shipping your software manuals to 127 countries
– solution; hire translators to translate
– would be much cheaper if a machine could do this!
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How hard is automated translation
– very difficult!
– e.g., English to Russian
– “The spirit is willing but the flesh is weak” (English)
– “the vodka is good but the meat is rotten” (Russian)
– not only must the words be translated, but their meaning also!
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Nonetheless....
– commercial systems can do alot of the work very well (e.g.,restricted
vocabularies in software documentation)
– algorithms which combine dictionaries, grammar models, etc.
– see for example babelfish.altavista.com
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Summary of Today’s Lecture
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Artificial Intelligence involves the study of:
– automated recognition and understanding of speech, images, etc
– learning and adaptation
– reasoning, planning, and decision-making
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AI has made substantial progress in
– recognition and learning
– some planning and reasoning problems
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AI Applications
– improvements in hardware and algorithms => AI applications in
industry, finance, medicine, and science.
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AI Research
– many problems still unsolved: AI is a fun research area!
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Assigned Reading
– Chapter 1 in the text
ICS-171:Notes 1: 29